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LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.
In: Sensors (14248220), Jg. 23 (2023-10-15), Heft 20, S. 8564-8575
Online
academicJournal
Zugriff:
In electronic warfare systems, detecting low-probability-of-intercept (LPI) radar signals poses a significant challenge due to the signal power being lower than the noise power. Techniques using statistical or deep learning models have been proposed for detecting low-power signals. However, as these methods overlook the inherent characteristics of radar signals, they possess limitations in radar signal detection performance. We introduce a deep learning-based detection model that capitalizes on the periodicity characteristic of radar signals. The periodic autocorrelation function (PACF) is an effective time-series data analysis method to capture the pulse repetition characteristic in the intercepted signal. Our detection model extracts radar signal features from PACF and then detects the signal using a neural network employing long short-term memory to effectively process time-series features. The simulation results show that our detection model outperforms existing deep learning-based models that use conventional autocorrelation function or spectrogram as an input. Furthermore, the robust feature extraction technique allows our proposed model to achieve high performance even with a shallow neural network architecture and provides a lighter model than existing models. [ABSTRACT FROM AUTHOR]
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LPI Radar Detection Based on Deep Learning Approach with Periodic Autocorrelation Function.
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Autor/in / Beteiligte Person: | Park, Do-Hyun ; Jeon, Min-Wook ; Shin, Da-Min ; Kim, Hyoung-Nam |
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Zeitschrift: | Sensors (14248220), Jg. 23 (2023-10-15), Heft 20, S. 8564-8575 |
Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s23208564 |
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